Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving

From Scientific Publications to Community Insights

Scientific research plays a crucial role in the development of a society. With ever-increasing volumes of scientific publications are now making it extremely challenging to analyze and maintain insights into the scientific communities like collaboration or citation trends and evolution of interests etc. This thesis is an effort towards using scientific publications to provide detailed insights into a scientific community from a range of aspects. The contribution of this thesis is five-fold. Firstly, this thesis proposes approaches for automatic information extraction from scientific publications. The proposed layout-based approach for this purpose is inspired by how human beings perceive individual references relying only on visual queues. The proposed approach significantly outperforms the existing text-based techniques and is independent of any domain or language. Secondly, this thesis tackles the problem of identifying meaningful topics from a given publication as the keywords provided in the publication are not always accurate representatives of the publication topic. To rectify this problem, this thesis proposes a state-of-the-art keywords extraction approach that employs a domain ontology along with the detected keywords to perform topic modeling for a given set of publications. Thirdly, this thesis analyses the disposition of each citation to understand its true essence. For this purpose, we proposes a transformer-based approach for analyzing the impact of each citation appearing in a scientific publication. The impact of a citation can be determined by the inherent sentiment and intent of a citation, which refers to the assessment and motive of an author towards citing a scientific publication. Furthermore, this thesis quantifies the influence of a research contributor in a scientific community by introducing a new semantic index for researchers that takes both quantitative and qualitative aspects of a citation into account to better represent the prestige of a researcher in a scientific community. Semantic Index is also evaluated for conformity to the guidelines and recommendations of various research funding organizations to assess the impact of a researcher. In this thesis, all of the aforementioned aspects are packaged together in a single framework called Academic Community Explorer (ACE) 2.0, which automatically extracts and analyzes information from scientific publications and visualizes the insights using several interactive visualizations. These visualizations provide an instant glimpse into the scientific communities from a wide range of aspects with different granularity levels

Additional Labeled Reference Data from the Linked Open Citation Database (LOC-DB) Project

The data consists of 2.402 pages of lists of references from books and chapters together with the labeled boxes for each entry in the list of references. The XML files contain the coordinates of the boxes and for each box a label (box or incomplete)

Linked Open Citation Database: Enabling Libraries to Contribute to an Open and Interconnected Citation Graph

Citations play a crucial role in the scientific discourse, in information retrieval, and in bibliometrics. Many initiatives are currently promoting the idea of having free and open citation data. Creation of citation data, however, is not part of the cataloguing workflow in libraries nowadays. In this paper, we present our project Linked Open Citation Database, in which we design distributed processes and a system infrastructure based on linked data technology. The goal is to show that efficiently cataloguing citations in libraries using a semi-automatic approach is possible. We specifically describe the current state of the workflow and its implementation. We show that we could significantly improve the automatic reference extraction that is crucial for the subsequent data curation. We further give insights on the curation and linking process and provide evaluation results that not only direct the further development of the project, but also allow us to discuss its overall feasibility

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Comment: 93 page